Method for optimizing weather resistance of polycarbonate material

Abstract

The invention provides a method for optimizing weather resistance of a polycarbonate material, which comprises the following steps: acquiring molecular structure data of a polycarbonate base material and environmental erosion parameters of an outdoor application scene, and constructing a fixed distribution model of internal protection components; according to the fixed distribution model, adjusting the chemical composition proportion of an external protective layer, and determining an optimized formula of deep fusion; testing the weather resistance of the enhanced material sample by simulating an outdoor application scene, and obtaining a quantitative index of the loss of the protective components; classifying potential factors of material stability according to the quantitative indexes to obtain a stability evaluation result; a prediction algorithm is adopted to analyze the influence degree of the lack of deep fusion on the application reliability, whether outdoor application scene requirements are met or not is judged, and a performance verification model is constructed; and adjusting process parameters of integral forming treatment through the performance verification model, obtaining weather resistance improvement data, and determining a stability optimization scheme in long-term use.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for optimizing the weather resistance of polycarbonate materials. Background Technology

[0002] In the field of high-performance optical materials, polycarbonate, as an engineering plastic with high transparency and excellent strength, is widely used in high-end applications such as automotive headlight covers, optical lenses, and explosion-proof windows, and its importance is self-evident. Especially in long-term outdoor environments, the material needs to withstand complex weather conditions and maintain its long-lasting transparency and mechanical properties. However, current technologies often reveal insurmountable shortcomings in addressing this requirement, limiting the further application of the material in key areas.

[0003] Existing methods often employ a step-by-step approach, such as first molding the substrate and then applying a surface coating to enhance its protective capabilities. While this method improves material performance to some extent, the separate processing steps can lead to weak interfacial bonding, potentially resulting in detachment or cracking over time. A deeper challenge lies in the fact that protective measures are often limited to a single surface or internal layer, failing to create a holistic and synergistic protective effect. This is particularly problematic when facing long-term environmental corrosion, as the material's stability and lifespan cannot be guaranteed.

[0004] Focusing on the technical challenges, the core issue lies in achieving long-term stability within the material while simultaneously constructing a protective layer that is tightly bonded to the substrate on the surface. Firstly, the internally added protective components are prone to loss over time, leading to a gradual weakening of the protective effect. This instability directly impacts the material's reliability during long-term use. Secondly, due to insufficient internal protection, the external protective layer needs to withstand greater environmental stress. However, in current processes, the bonding between the external layer and the substrate often relies on physical adhesion, lacking deep interfacial fusion. This makes the protective layer prone to failure under external forces or thermal expansion and contraction. For example, in the practical application of automotive headlight covers, the material needs to operate in environments with alternating high and low temperatures and intense ultraviolet radiation. Loss of internal protective components can cause the material to yellow, while a weakly bonded external protective layer may peel off under temperature changes, ultimately causing the headlight cover to lose its transparency and protective function.

[0005] Therefore, achieving both the long-term fixation of internal protective components and the deep integration of the external protective layer with the substrate during material preparation has become a key issue in improving the long-term weather resistance of polycarbonate materials. Solving this problem not only relates to breakthroughs in the material's performance but also directly impacts its reliability and market competitiveness in high-end applications. Summary of the Invention

[0006] This invention provides a method for optimizing the weather resistance of polycarbonate materials, mainly comprising:

[0007] Molecular structure data of polycarbonate substrate and environmental erosion parameters for outdoor applications are obtained to construct a fixed distribution model of internal protective components. Based on the fixed distribution model, the chemical composition ratio of the external protective layer is adjusted to determine the optimized formulation for deep fusion. The polycarbonate substrate is integrally molded using the optimized formulation, interface bonding data is extracted, and the risk level is assessed to obtain reinforced material samples. The weather resistance of the reinforced material samples is tested by simulating outdoor application scenarios to obtain quantitative indicators of protective component loss. Based on the quantitative indicators, potential factors affecting material stability are classified to obtain stability assessment results. A predictive algorithm is used to analyze the impact of lack of deep fusion on application reliability, determine whether it meets the requirements of outdoor application scenarios, and construct a performance verification model. The process parameters of the integral molding process are adjusted using the performance verification model to obtain weather resistance improvement data and determine a stability optimization scheme for long-term use. Furthermore, the step of acquiring molecular structure data of the polycarbonate substrate and environmental erosion parameters for outdoor applications, and constructing a fixed distribution model for the internal protective components, includes: collecting the molecular chain arrangement characteristics of the polycarbonate substrate and the temperature and humidity variation range of the environmental erosion parameters to generate a corresponding dataset; for the dataset, using molecular dynamics simulation methods to analyze the hydrogen bonding forces between the internal protective components and the polycarbonate substrate, calculating the distance between hydrogen atoms and oxygen atoms, determining the intensity distribution map of the hydrogen bonding forces, and extracting the coordinates of the attachment points of the protective components; obtaining density gradient values ​​from the attachment point coordinates, and determining whether the density difference between adjacent points exceeds a preset threshold; if it exceeds, adjusting the molecular spacing of the internal protective components to obtain the adjusted molecular spacing configuration; based on the interaction response curve between the adjusted molecular spacing configuration and the environmental erosion parameters, using finite element analysis to simulate stability fluctuations and determine the fixed mode of the protective components; for the fixed mode of the protective components, constructing a spatial grid framework, integrating substrate bonding strength data, and obtaining the fixed distribution model of the internal protective components.Furthermore, the step of adjusting the chemical composition ratio of the outer protective layer according to the fixed distribution model to determine the optimized formulation for deep fusion includes: obtaining chemical composition data of the outer protective layer from the fixed distribution model; using detection tools such as scanning electron microscopy and surface tension testing to determine the interface energy difference between the outer protective layer and the polycarbonate substrate to obtain an initial energy difference result; comparing the initial energy difference result with a preset threshold, and if it exceeds the preset threshold, recording the range of values ​​exceeding the threshold to determine the direction of chemical composition adjustment; gradually changing the chemical ratio of the outer protective layer for the chemical composition direction, verifying the change in interface energy difference after each adjustment through a layered testing method, and obtaining the adjusted energy difference data; based on the adjusted energy difference data, combined with the substrate matching characteristics and the stability requirements of the protective layer, screening chemical ratio combinations that meet the preset threshold to obtain a preliminary optimized formulation scheme; for the preliminary optimized formulation scheme, introducing the influencing factor of the protective layer thickness, and determining the final optimized formulation by hot-pressing composite and interface observation under simulated application conditions. Furthermore, the step of using the optimized formula to integrally mold the polycarbonate substrate, extracting interfacial bonding data, and determining the risk level to obtain a reinforced material sample includes: obtaining the initial formula parameters of the polycarbonate substrate from a pre-established formula database; performing integral molding using a hot-pressing device with temperature control to obtain an interfacial bonding strength value; based on the interfacial bonding strength value, using a surface scanning tool to extract real-time interfacial bonding data through optical imaging to determine the risk level of poor interfacial bonding and obtain a risk assessment result; if the risk assessment result exceeds a preset threshold, adjusting the additive content in the formula ratio, verifying the adjusted interfacial bonding data through repeated molding and energy dispersive spectroscopy, and determining an optimized formula scheme; using the optimized formula scheme to reinforce the polycarbonate substrate, obtaining fusion depth data through thickness measurement, and obtaining the reinforced material sample. Furthermore, the step of testing the weather resistance of the enhanced material sample by simulating outdoor application scenarios to obtain quantitative indicators of the loss of protective components includes: using ultraviolet irradiation equipment to simulate outdoor application scenarios for the enhanced material sample and obtaining long-term weather resistance decay curve data; based on the decay curve data, extracting the initial change value of the loss of protective components through component spectrum analysis to obtain a loss trend index; for the loss trend index, combining environmental factor calibration and adjustment quantitative calculations to determine the intermediate quantitative level of the loss of protective components; and from the intermediate quantitative level, verifying the fusion depth data through repeated irradiation to determine the quantitative index of the loss of protective components.Furthermore, the step of classifying potential factors of material stability based on the quantitative index to obtain a stability assessment result includes: collecting data on the loss of protective components from the enhanced material sample and calculating a quantitative index of the loss of protective components; if the quantitative index is lower than a preset threshold, obtaining potential factor data from environmental exposure records to determine a factor dataset; applying a support vector machine algorithm to classify potential factors of material stability for the factor dataset to obtain classified factor groups; based on the classified factor groups, fusing aging rate indicators obtained from historical aging records to determine the material stability assessment result; extracting key thresholds from the material stability assessment result, generating a protective component replenishment scheme, and obtaining the final optimized stability distribution. Furthermore, the step of using a predictive algorithm to analyze the impact of lack of deep fusion on application reliability, determine whether it meets the requirements of outdoor application scenarios, and construct a performance verification model includes: processing the data features of lack of deep fusion using a random forest algorithm based on the stability assessment results to obtain a predicted value of the degree of reliability impact; obtaining integrated environmental variable data for the outdoor application scenario based on the predicted value; if the integrated data exceeds a preset threshold, adjusting the influencing factor analysis based on the predicted value to determine the threshold range for scenario adaptation verification; for the threshold range, merging the integrated environmental variable data and the predicted value through real-time data calibration to determine whether it meets the requirements of the outdoor application scenario; obtaining a subset that meets the requirements from the determination, optimizing the subset through model fusion to obtain a preliminary performance verification model; iterating the preliminary performance verification model through algorithm training data, adjusting feature weights, and obtaining a final performance verification model. Furthermore, the step of adjusting the process parameters of the integral molding process through the performance verification model to obtain weather resistance improvement data and determine the stability optimization scheme for long-term use includes: adjusting the combination of process parameters for the integral molding process, recording the initial optical performance under different parameters, and obtaining a preliminary performance data set; constructing a performance verification model based on the preliminary performance data set, fitting the relationship between environmental variables through a linear regression method, outputting predicted values, simulating long-term use test scenarios, and obtaining weather resistance performance values; extracting key weather resistance data features based on the weather resistance performance values ​​and environmental adaptability assessment, and determining the changing trend of the material's weather resistance characteristics; adjusting the combination of process parameters based on the changing trend and a parameter iteration method to optimize the integral molding process and obtain improved optical performance data; and formulating a stability optimization scheme based on the improved optical performance data to determine the stable performance of the material in long-term use.Furthermore, the step of acquiring molecular structure data of the polycarbonate substrate and environmental erosion parameters for outdoor application scenarios, and constructing a fixed distribution model for the internal protective components, includes: collecting initial molecular structure data of the polycarbonate substrate, analyzing molecular chain arrangement characteristics, and combining this with the temperature and humidity variation range of the environmental erosion parameters to form a corresponding dataset; for the corresponding dataset, using molecular dynamics simulation methods to calculate the bonding force between the internal protective components and the polycarbonate substrate, generating an intensity distribution map, and extracting the coordinates of the attachment points of the protective components; based on the coordinates of the attachment points, analyzing the density gradient value, and determining whether the density difference between adjacent points meets a preset threshold; if not, adjusting the intermolecular spacing to obtain the adjusted configuration; through the interaction response between the adjusted configuration and the environmental erosion parameters, using finite element analysis methods to divide the grid elements and calculate the stress distribution, determining the fixed mode under stability fluctuations; for the fixed mode, constructing a spatial grid framework, integrating the bonding strength data, and generating the fixed distribution model. Furthermore, the step of adjusting the chemical composition ratio of the outer protective layer according to the fixed distribution model to determine the optimized formula for deep fusion includes: extracting the chemical composition data of the outer protective layer from the fixed distribution model; measuring the interface energy difference using a pre-established detection tool to obtain initial results; comparing the initial results with a preset threshold; if the initial results exceed the preset threshold, determining the adjustment direction and gradually changing the chemical ratio; verifying the change in the interface energy difference after adjustment using a layered testing method to obtain updated data; based on the updated data, combined with the substrate surface roughness and chemical affinity, as well as the durability and adhesion requirements of the protective layer, selecting a ratio combination that meets the preset threshold to obtain a preliminary scheme; for the preliminary scheme, introducing thickness influencing factors, verifying the fusion effect through hot-pressing composite and interface observation, and determining the final optimized formula.

[0008] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0009] This invention discloses an optimization method for the outdoor weather resistance of polycarbonate materials, aiming to solve business problems such as the loss of protective components, weak interfacial bonding, and decreased stability caused by environmental erosion during long-term use. By analyzing the bonding strength between the internal protective components and the substrate through molecular simulation, a fixed distribution model is established, and the external protective layer formulation is optimized based on the interfacial energy difference to achieve deep integration. A one-piece molding process is adopted, and interfacial bonding data is monitored in real time. Long-term weather resistance is simulated using ultraviolet irradiation to quantify the loss index of protective components. Furthermore, support vector machines and random forest algorithms are used to evaluate material stability and application reliability, and process parameters are iteratively adjusted to ultimately form a high-performance optical weather resistance enhancement solution. This invention, through multi-level technological integration, ensures the long-term stability of materials in outdoor environments, significantly improving weather resistance and application reliability. Attached Figure Description

[0010] Figure 1 This is a flowchart of a method for optimizing the weather resistance of a polycarbonate material according to the present invention. Detailed Implementation

[0011] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0012] like Figure 1 The method for optimizing the weather resistance of a polycarbonate material in this embodiment may specifically include:

[0013] S101. By collecting initial molecular structure data of polycarbonate substrate and environmental erosion parameters under outdoor application scenarios, molecular simulation methods are used to analyze the bonding strength between the internal protective components and the substrate, and a fixed distribution model of the internal protective components is obtained.

[0014] By collecting initial molecular structure data of the polycarbonate substrate and environmental erosion parameters under outdoor application scenarios, the molecular chain arrangement characteristics of the polycarbonate substrate and the temperature and humidity variation range of the environmental erosion parameters are obtained, resulting in a corresponding dataset of molecular chain arrangement characteristics and temperature and humidity variation ranges. For this dataset, molecular dynamics simulation is used to analyze the hydrogen bonding forces between the internal protective component and the polycarbonate substrate. The intensity distribution map of the hydrogen bonding forces is determined by calculating the distance between hydrogen and oxygen atoms, obtaining the coordinates of the attachment points of the protective component in the intensity distribution map. The density gradient value of the protective component is calculated from the attachment point coordinates using the formula G = (D2 - D1) / (r2 - r1), where G is the density gradient value, D1 and D2 are the density values ​​of adjacent points, and r1 and r2 are the corresponding coordinate distances. By comparing the density differences between adjacent points, it is determined whether the density gradient value exceeds a preset threshold. If it does, the intermolecular spacing of the internal protective component is adjusted to obtain the adjusted intermolecular spacing configuration. The interaction response curve between the adjusted intermolecular spacing configuration and the environmental erosion parameters is obtained. The stability fluctuation of the interaction response curve is simulated using finite element analysis (FEM). The FEM is used to input the interaction response curve, divide the data into mesh elements, calculate the stress distribution, and determine the fixed mode of the protective component under stability fluctuations. For the fixed mode of the protective component, a spatial mesh framework of the internal protective component is constructed within the polycarbonate substrate. The substrate bonding strength data is integrated from the spatial mesh framework to obtain a fixed distribution model of the internal protective component.

[0015] In one embodiment, initial molecular structure data of the polycarbonate substrate is acquired by scanning electron microscopy or X-ray diffraction, including parameters such as the arrangement of molecular chains, bond lengths, and bond angles.

[0016] Specifically, a polycarbonate sample is first prepared and placed in a vacuum environment. The surface is then scanned with an electron beam to obtain high-resolution images. The image data is then analyzed using software to extract the geometric model of the molecular structure. This acquisition method ensures data accuracy and provides a reliable foundation for subsequent simulations. Furthermore, environmental erosion parameters for outdoor applications include ultraviolet radiation intensity, temperature fluctuation range, and humidity levels. For example, in building exterior wall applications, ultraviolet data is collected throughout the year to quantify its degradation impact on the substrate.

[0017] For example, when analyzing the bonding strength between the internal protective component and the substrate using molecular simulation methods, molecular dynamics simulation software such as GROMACS can be used. First, a system model is constructed, placing the polycarbonate molecular chains and the protective component (such as UV-resistant additives) within a simulation chamber, and force field parameters are applied to describe the interatomic interactions. Then, the simulation is run, the binding energy is calculated, and the bonding stability is evaluated through trajectory analysis. The key to this method is selecting a suitable force field, such as OPLS-AA, to simulate molecular behavior under real-world conditions.

[0018] It should be noted that the bonding strength is quantified by calculating the difference in free energy, such as the contribution of hydrogen bonds or van der Waals forces between the protective component and the substrate, to ensure that the simulation results reflect actual outdoor durability.

[0019] In one possible implementation, a fixed distribution model of the internal protective component is obtained based on simulation results. The specific process involves extracting location data from the simulation trajectory and analyzing the component's distribution density within the substrate using statistical methods such as the radial distribution function. A three-dimensional model is then constructed, displaying a uniform or gradient distribution of the component; for example, in outdoor signage applications, the model shows the protective component concentrated in the surface layer to resist UV corrosion. This model generation emphasizes the stability of the distribution, achieved through iterative parameter optimization.

[0020] Preferably, in another embodiment, environmental erosion parameters are adjusted for different outdoor scenarios.

[0021] For example, in solar panel applications, wind and sand erosion parameters are added to the simulation to analyze how the protective components enhance the substrate's abrasion resistance. The simulation process is similar to that described above, but an additional particle collision model is introduced to calculate changes in bonding strength, thereby updating the distribution model to adapt to specific scenarios. Furthermore, this method can be extended to various polycarbonate products, such as greenhouse covering materials. In this scenario, the collected molecular structure data focuses on a high-transparency substrate, and environmental parameters include high temperature and high humidity conditions. Molecular simulation analysis shows that the protective components are fixed in distribution through covalent bonds, improving overall weather resistance.

[0022] Understandably, the output of the distribution model is a digital mesh file, which facilitates subsequent manufacturing applications. Through the above steps, precise control over the distribution of protective components can be achieved.

[0023] In one embodiment, the principle of molecular simulation is explained in detail: molecular simulation is based on classical mechanics and simulates the trajectories of atoms to predict macroscopic properties.

[0024] Specifically, after importing the initial molecular structure data, periodic boundary conditions are set, and a simulation lasting several nanoseconds is run, during which the energy minimization process is monitored. This is combined with intensity analysis involving the calculation of potential functions, such as the Lennard-Jones potential, to quantify nonbonded interactions. This detailed simulation ensures that the model accurately reflects how components are fixed under outdoor erosion; for example, in building curtain walls, the model guides the uniform distribution of additives to extend service life. The technical benefit of this process is improved material durability without introducing subjective optimization.

[0025] For example, in simulation analysis, if the environmental erosion parameters include the effect of acid rain, the corrosion effect of pH on bonding strength is calculated additionally, and the model is adjusted to strengthen the protective layer.

[0026] Specifically, the verification of the fixed distribution model is achieved through experimental comparison, such as accelerated aging tests, to confirm that the simulated predicted distribution is consistent with the actual distribution.

[0027] S102. Based on the fixed distribution model of the internal protective components, obtain the chemical composition information of the external protective layer. If the interface energy difference between the external protective layer and the substrate exceeds the preset threshold, adjust the composition ratio and determine the optimized formula for deep fusion.

[0028] Chemical composition data of the external protective layer is obtained from the internal component distribution model. Based on this data, a pre-established detection tool is used to measure the interfacial energy difference between the external protective layer and the substrate using scanning electron microscopy combined with surface tension testing, obtaining an initial energy difference result. This initial energy difference result is compared with a preset threshold standard. If the energy difference exceeds the preset threshold standard, the specific range of the excess value is recorded, determining the chemical composition direction that needs adjustment, including the polymer ratio and additive content. For the chemical composition direction requiring adjustment, the chemical ratio of the external protective layer is gradually changed. A layer-by-layer testing method is used to verify the change in interfacial energy difference after each adjustment through layer-by-layer peeling and energy dispersive spectroscopy analysis, obtaining the adjusted energy difference data. Based on the adjusted energy difference data, combined with the substrate matching characteristics and the protective layer stability requirements (where substrate matching characteristics refer to surface roughness and chemical affinity, and protective layer stability requirements refer to durability and adhesion), chemical ratio combinations that meet the preset threshold standard are screened to determine the preliminary optimized formulation scheme. For the preliminary optimized formulation, the influencing factors of the protective layer thickness were introduced, and the deep fusion technology was verified by hot pressing and interface observation to simulate the fusion effect in the actual application environment, and the final optimized formulation was determined.

[0029] In one embodiment, a fixed distribution model of the internal protective components is used to describe the spatial distribution of various chemical components within the polycarbonate substrate. This model, based on statistical distribution principles, establishes a functional relationship between component concentration and depth.

[0030] Specifically, by collecting cross-sectional data of the protective layer sample, and using microscopic observation and chemical analysis instruments to measure the concentration of each component at different depths, a fixed distribution model is constructed.

[0031] For example, this model can be represented as a mapping between component concentration and depth, ensuring the stability of the internal protective structure. Furthermore, based on the aforementioned fixed distribution model, the chemical composition information of the outer protective layer is obtained. This process involves elemental analysis of an outer layer sample.

[0032] For example, X-ray fluorescence spectrometry or electron probe microanalysis is used to detect the proportions of major components in the outer layer, such as the polymer matrix, fillers, and additives. These analyses yield chemical composition data for the outer layer, including the percentage content of each element and molecular structural characteristics. This method of acquisition helps in understanding the compatibility between the outer layer and the internal structure.

[0033] It should be noted that the interface energy difference refers to the degree of energy mismatch at the interface between the outer protective layer and the substrate. This concept originates from surface physics and reflects the bonding strength between two materials at the contact surface. The process of calculating the interface energy difference involves first measuring the surface energy of the outer layer and the surface energy of the substrate, and then calculating the difference between them.

[0034] Specifically, surface energy can be obtained through contact angle measurement, for example, by dropping a liquid onto a surface and calculating the contact angle, thus deriving the surface energy value. If the difference exceeds a preset threshold, such as 5 joules per square meter, it indicates poor fusion and requires adjustment.

[0035] In one possible implementation, if the interfacial energy difference exceeds a preset threshold, the composition ratio of the outer protective layer is adjusted. This adjustment optimizes the chemical composition information, for example, by increasing the proportion of compatible additives or changing the polymer type. The specific process involves iterative calculations: first, simulating the interfacial energy changes under different ratios, then using software tools to predict the adjusted difference until it falls below the threshold. In this way, precise control of the composition ratio is achieved, ensuring a more stable interface between the outer layer and the substrate.

[0036] Preferably, determining the optimized formula for deep integration involves the integrated and adjusted composition ratios and a fixed distribution model.

[0037] For example, in the field of anti-corrosion coatings, an optimized formulation may include specific proportions of epoxy resin, silane coupling agent, and filler. Experimental verification has shown that this formulation forms a uniform, fused layer on a substrate such as a metal surface, improving corrosion resistance.

[0038] Understandably, in another embodiment, this method is applied to building protective coating scenarios. After obtaining the composition of the outer layer based on a fixed distribution model, the interfacial energy difference is evaluated. If it exceeds a threshold, the filler ratio is adjusted to ultimately obtain an optimized formula, thereby improving the adhesion between the coating and the concrete substrate.

[0039] Specifically, the construction of the fixed distribution model requires detailed consideration of the component diffusion mechanism. This model assumes that the components follow a Gaussian distribution within the protective layer, with concentrations gradually decreasing from the surface to the interior. During construction, multi-point data is first collected, distribution curves are fitted, and then the model's accuracy is verified. This interpretation helps to understand how the model fixes the internal component distribution, providing a foundation for subsequent optimization of the outer layer. Furthermore, the detailed process of adjusting the composition ratio includes quantitative analysis. First, the current proportions of the components are listed, such as 60% resin and 30% filler. Then, based on energy difference feedback, specific components are gradually increased or decreased. Through multiple simulation iterations, the optimal proportion is determined, for example, increasing the additive to 10% to reduce the interfacial energy difference. This mechanism ensures deep integration, achieving long-term durability of the protective layer.

[0040] For example, in protective coatings for industrial equipment, applying this method and optimizing the formulation can significantly improve the fusion effect and extend the service life of the equipment.

[0041] In one embodiment, the logical flow from model building to formula determination ensures continuity, and the results are continuously refined and optimized through data feedback loops.

[0042] S103. The polycarbonate material is integrally molded using an optimized formula. Real-time interface bonding data is extracted from the molding process to determine the risk level of poor interface bonding and obtain a reinforced material sample.

[0043] A pre-established formulation database is constructed based on historical experimental data and literature. Initial formulation parameters for polycarbonate materials are obtained from this database. Based on these initial formulation parameters, a one-piece molding process is performed using a hot-pressing device with temperature control to obtain the interfacial bonding strength value during the molding process. According to the interfacial bonding strength value, real-time interfacial bonding data is extracted using optical imaging with a surface scanning tool to determine the risk level of poor interfacial bonding and obtain a risk assessment result. If the risk level exceeds a preset threshold, the additive content in the formulation is adjusted. The adjusted interfacial bonding data is verified by repeated molding combined with energy dispersive spectroscopy to determine the optimized formulation scheme. Using the optimized formulation scheme, the polycarbonate material is reinforced. Fusion depth data is obtained from the treatment through thickness measurement to obtain a reinforced material sample.

[0044] In one embodiment, for the integral molding process of polycarbonate materials, it is first necessary to improve the material performance and the interfacial bonding strength during the molding process by optimizing the formula.

[0045] Specifically, the process of optimizing the formulation involves adjusting the proportions of polycarbonate matrix materials, toughening agents, stabilizers, and other additives to suit different molding conditions and application requirements.

[0046] For example, in industrial production, polycarbonate raw materials with appropriate molecular weights can be selected based on the mechanical performance requirements of the target product, and a certain proportion of toughening agents, such as acrylate copolymers, can be added to improve the material's impact resistance. Simultaneously, antioxidants and heat stabilizers are added to ensure that the material does not degrade during high-temperature molding. Through multiple experiments and performance tests, the proportion range of each component is determined, resulting in an optimized formulation suitable for unibody molding. Furthermore, in the unibody molding process, key process parameters must be controlled to ensure molding quality.

[0047] For example, in injection molding, parameters such as mold temperature, injection pressure, and cooling time need to be precisely controlled to reduce internal stress and improve interfacial bonding strength. Mold temperature is typically controlled within a certain range to ensure that the polycarbonate material flows uniformly and fully fills the mold cavity after injection. Injection pressure needs to be adjusted according to material flowability and mold structure to avoid defects such as bubbles or shrinkage marks. The choice of cooling time directly affects the dimensional stability and interfacial bonding effect of the molded part. Fine-tuning these parameters can significantly improve the overall performance of the molded part. In another possible implementation, real-time extraction of interfacial bonding data during the molding process is a crucial step in ensuring quality control.

[0048] Specifically, interface bonding data mainly refers to the bonding strength of the molded part in different regions, the distribution of interface defects, and information on possible microcracks. This data can be obtained through embedded sensors or non-destructive testing equipment.

[0049] For example, in injection molding equipment, pressure and temperature sensors can be installed at key locations in the mold to monitor pressure changes and temperature distribution of the material during the molding process in real time, indirectly reflecting the interfacial bonding state. Furthermore, ultrasonic testing technology can be combined to scan the interfacial area after molding, obtaining more accurate bonding quality data. This data collection provides a reliable basis for subsequent risk assessment.

[0050] It should be noted that determining the risk level of interface bonding issues based on the collected interface combination data is one of the core steps of the technical solution.

[0051] In one embodiment, risk assessment can be achieved by constructing a data analysis model.

[0052] Specifically, the collected pressure, temperature, and other data are first preprocessed to remove outliers and standardize the data. Then, the processed data is input into a pre-defined risk assessment model. This model, built upon historical data and experimental results, includes multiple assessment indicators such as the uniformity of interfacial pressure distribution and the rate of change of temperature gradient. Through comprehensive analysis of these indicators, the model outputs a risk level value to characterize the likelihood of poor interfacial bonding.

[0053] For example, if uneven pressure distribution and temperature gradient exceeding the safe range are detected in the interface area during a molding process, the model may output a high risk level, indicating that process parameters or formula ratios need to be adjusted.

[0054] Preferably, based on the risk assessment, targeted measures can be further taken to reduce the risk of poor interfacial bonding. In one embodiment, if the risk level is high, the interfacial bonding quality can be improved by adjusting the injection pressure or extending the cooling time. Furthermore, the formulation can be fine-tuned, for example, by increasing the proportion of toughening agents to improve material toughness, thereby reducing the possibility of interfacial cracking. These adjustments need to be dynamically optimized in conjunction with real-time data feedback to ensure the stability of the molding process. Through the above methods, the quality defect rate during production can be effectively reduced. In another embodiment, different combinations of process parameters can be used to adapt to the specific requirements of polycarbonate material molding for different application scenarios.

[0055] For example, when producing high-transparency optical components, special attention must be paid to the surface finish of the mold and the cooling rate to avoid fogging or microcracks in the interface area. When producing high-strength structural parts, it is crucial to optimize the injection pressure and toughening agent ratio to ensure that the interfacial bonding strength meets usage requirements. These parameter adjustments for different scenarios are all based on the aforementioned data collection and risk assessment results, ensuring the versatility and adaptability of the technical solutions.

[0056] Specifically, after optimizing the molding process and controlling risks, reinforced polycarbonate material samples can be obtained. These samples need to undergo a series of performance tests to verify their quality.

[0057] For example, tensile tests, impact tests, and interfacial bond strength tests can be used to evaluate the mechanical properties and durability of samples. The test results can serve as feedback data for further optimization of formulations and process parameters. This closed-loop optimization mechanism allows for continuous improvement of the overall performance of material samples.

[0058] For example, in industrial production, after optimizing the formulation and adjusting the process, the interfacial bonding strength of a batch of polycarbonate material samples increased significantly compared to the unoptimized version. This improvement was verified using standard testing methods, demonstrating the high reliability of the technical solution in practical applications. The enhanced effect of the samples is not only reflected in mechanical properties but also in their stability and weather resistance during long-term use, providing technical support for subsequent large-scale production.

[0059] Understandably, the above-mentioned technical solutions are applicable to various scenarios in the polycarbonate material processing field. Whether producing small precision parts or large structural components, precise control of interface bonding quality can be achieved by adjusting the formula and process parameters, combined with real-time data acquisition and risk assessment. This flexibility ensures the wide adaptability of the technical solutions in practical applications. Furthermore, through the comprehensive application of the above-mentioned implementation methods, production efficiency can be improved and scrap rates reduced while ensuring molding quality. In specific production practices, suitable data acquisition methods and risk assessment models can be flexibly selected according to equipment conditions and product requirements to ensure that the implementation effect of the technical solutions achieves the expected goals, providing reliable technical support for the integrated molding process of polycarbonate materials.

[0060] S104. For the reinforced material samples, simulate outdoor application scenarios by ultraviolet irradiation to obtain long-term weather resistance degradation curve data and determine the quantitative index of protective component loss.

[0061] For the reinforced material samples, ultraviolet irradiation equipment was used to simulate outdoor application scenarios, and the decay curve data of long-term weather resistance was obtained from the irradiation process. Based on the decay curve data, the initial change value of the loss of protective components was extracted by component spectral analysis such as Fourier transform infrared spectroscopy, and a loss trend index was obtained. For the loss trend index, calibration and adjustment quantification calculations were performed in combination with environmental factors such as temperature and humidity. For example, the formula L=I*(1-e^(-k*t)) was used, where L is the loss amount, I is the initial value, k is the decay constant, and t is time, to determine the intermediate quantification level of the loss of protective components, that is, the preliminary value of the loss trend index after environmental calibration. From the intermediate quantification level, the fusion depth data, that is, the integrated result of multiple rounds of irradiation data, was verified by repeated irradiation to determine the quantification index of the loss of protective components.

[0062] In one implementation, the long-term weather resistance test of the reinforced material sample first requires simulating outdoor application scenarios by irradiating with ultraviolet light to assess the changes in the material's performance in the natural environment.

[0063] Specifically, an ultraviolet (UV) aging test chamber is used as the main equipment. By setting specific irradiation intensity, temperature, and humidity conditions, the aging process under outdoor sunlight is simulated. The wavelength range of the UV lamps inside the chamber is typically controlled between 280 and 400 nanometers to cover the ultraviolet portion of sunlight. The irradiation time can be set to hundreds to thousands of hours according to testing requirements, during which the performance parameters of the material samples are recorded periodically. This simulation method can effectively reproduce the corrosive effects of the outdoor environment on materials, providing a reliable experimental basis for subsequent performance analysis. Based on the above simulation conditions, obtaining the long-term weather resistance degradation curve is one of the core steps in the entire testing process.

[0064] For example, tensile strength, surface color difference, and mass loss rate were selected as key performance indicators for reinforced material samples, and data were obtained through periodic testing. During the testing process, the material samples were placed in a UV aging chamber, and a portion of the samples was taken out for performance testing at regular intervals, such as every 100 hours. Tensile strength was measured using a universal testing machine, surface color difference was quantified using a colorimeter, and mass loss rate was calculated by weighing using a precision balance. The obtained data were recorded and plotted as time-performance change curves, and the performance degradation trend was analyzed using curve fitting methods. This method can intuitively reflect the performance degradation law of materials under long-term UV irradiation, providing a scientific basis for material durability assessment.

[0065] It should be noted that the testing cycle and sampling frequency can be adjusted according to the material type and application scenario to ensure the representativeness and accuracy of the data.

[0066] In one possible implementation, different test parameters can be used to obtain attenuation curves for different types of reinforcing materials, such as polymer-based composites and metal-coated materials.

[0067] For example, for polymer-based materials, the focus is on the impact of UV-induced molecular chain breakage on tensile strength; while for metallic coating materials, the emphasis is on surface oxidation and color variation. This differentiated testing allows for a comprehensive evaluation of the weather resistance of various materials in simulated outdoor environments.

[0068] Preferably, determining the quantitative indicators for preventing the loss of protective components is another key technical aspect.

[0069] Specifically, protective components typically refer to UV stabilizers or antioxidants added to materials, and their leaching directly affects the material's weather resistance. In experiments, chemical analysis methods, such as high-performance liquid chromatography (HPLC), can be used to detect the residual amount of protective components in material samples. Before testing, the component content of the initial sample is determined. Subsequently, samples are taken at different UV irradiation time points to analyze the reduction in protective components and calculate the leaching rate as a quantitative indicator. Furthermore, the correlation between the leaching of protective components and performance degradation can be analyzed by combining this data with material performance degradation data. This method can provide data support for optimizing material formulations and lay the foundation for improving subsequent protective measures.

[0070] It should be noted that the testing methods and the number of sampling points need to be rationally designed based on the material characteristics and experimental conditions in order to improve the accuracy of the quantitative indicators.

[0071] In one embodiment, for polymer-based reinforced material samples, the test for protection against component loss can be supplemented by infrared spectroscopy analysis.

[0072] For example, the loss of UV stabilizers can be indirectly determined by detecting changes in the absorption peaks of specific chemical bonds on the material surface using an infrared spectroscopy method. This method, combined with high-performance liquid chromatography (HPLC), improves the reliability of the detection results and is applicable to different types of protective components. Furthermore, in another embodiment, for fiber-reinforced composite materials, the loss of protective components at the fiber-matrix interface can be the primary focus.

[0073] Specifically, scanning electron microscopy was used to observe changes in the interfacial microstructure, combined with chemical composition analysis, to assess the impact of ultraviolet radiation on the interfacial protective layer. This method can reveal the microscopic mechanisms of protective component loss, providing a more in-depth reference for materials design.

[0074] It is understandable that the aforementioned testing methods and quantitative indicators are applicable to various types of reinforcing materials, covering a wide range of application scenarios from polymer-based to metal-based. In one implementation, the intensity of ultraviolet irradiation and the testing cycle can be adjusted to simulate specific outdoor environments, such as extreme conditions in high-altitude or coastal areas, to meet different application requirements. In another implementation, for reinforcing materials exposed to high temperature and humidity environments for extended periods, humidity cycling tests can be added concurrently with ultraviolet irradiation to more realistically simulate outdoor conditions in tropical regions. By recording performance degradation data and the loss of protective components, the combined impact of environmental factors can be analyzed, providing guidance for the application of materials in specific scenarios.

[0075] Specifically, the attenuation curves and quantitative indicators obtained through the above methods can provide comprehensive data support for evaluating the weather resistance of reinforced materials. Especially in the analysis of the correlation between the loss of protective components and performance degradation, it can provide important basis for improving material formulations and optimizing protective technologies. Finally...

[0076] In one possible implementation, the test results described above can be used to guide the selection and maintenance strategies of reinforcing materials in outdoor applications.

[0077] For example, by predicting the service life of materials based on the attenuation curve and combining it with the loss index of protective components, a reasonable replacement or maintenance cycle can be formulated, thereby extending the service life of materials and reducing maintenance costs.

[0078] S105. If the quantitative index of the loss of protective components is lower than the preset threshold, the support vector machine algorithm is applied to classify the potential factors of the material stability limit (i.e. the limit threshold of material stability) and obtain the stability assessment result after classification.

[0079] Based on the quantification index of protective component loss determined in S104, if the quantification index is lower than a preset threshold of 0.5, potential factor data is obtained from environmental exposure records to determine a factor dataset. For the factor dataset, a support vector machine (SVM) algorithm is applied to classify the potential factors affecting material stability limits, resulting in classified factor groups. The SVM algorithm takes the factor dataset as input and outputs the classified factor groups; the material stability limit refers to the boundary value for material stability assessment. Based on the classified factor groups, the aging rate index obtained from historical aging records is integrated to determine the material stability assessment result, resulting in a classified stability assessment result, where the stability limit value is a key threshold in the assessment result.

[0080] In one implementation, the quantitative indicator of the loss of protective components can be calculated by monitoring changes in the chemical composition of the protective layer on the material surface.

[0081] Specifically, material samples are placed under simulated environmental conditions, such as a humidity-controlled laboratory environment. Samples are collected periodically, and a spectrometer is used to measure the remaining amounts of key protective components, such as the concentration of preservatives or antioxidants. Then, the leaching rate is obtained as a quantitative indicator by dividing the difference between the initial and current concentrations by the time interval. This method ensures the objectivity and repeatability of the indicator, providing a reliable basis for subsequent judgments. Furthermore, preset thresholds are set based on material type and application scenario.

[0082] For example, in the field of building material protection, the threshold can be set at 10 percent of the loss rate. If the quantitative index is lower than this threshold, it indicates that the protective components are severely lost, which may lead to a decrease in material stability.

[0083] It should be noted that this threshold can be determined through historical data analysis or industry standards, such as referring to the durability requirements in national building materials specifications, thereby avoiding subjective bias.

[0084] Preferably, when the quantified index is below a preset threshold, a support vector machine (SVM) algorithm is applied to classify the potential factors affecting the material's stability limit. The SVM algorithm is a supervised learning method that constructs a hyperplane to separate data points of different categories of potential factors. The specific process involves first collecting a dataset of potential factors, such as temperature, humidity, and chemical pollutant concentrations, which are treated as feature vectors. Then, the dataset is divided into a training set and a test set. In the training set, a kernel function (such as a radial basis function) is used to handle the nonlinear classification problem. The algorithm aims to maximize the margin between different categories, thereby accurately classifying which factors are most likely to cause a decrease in the stability limit; for example, classifying high temperature factors as a high-risk group.

[0085] In one possible implementation, the classification of potential factors takes into account the material’s performance in different protection scenarios.

[0086] For example, for bridge steel structure materials, potential factors might include salt spray corrosion and mechanical stress. The algorithm calculates support vectors based on the input feature vectors and outputs classification labels, such as "environmentally dominant" or "component-dominated." This classification helps identify the specific causes of stability limitations, ensuring the relevance of the assessment results.

[0087] For example, the Support Vector Machine (SVM) algorithm can be integrated into a materials testing system. First, the system collects quantitative index data; if the data is below a threshold, it automatically imports a latent factor dataset. Next, the algorithm performs feature scaling to normalize the data, preventing dimensional differences from affecting classification accuracy. Then, it optimizes model parameters, such as regularization coefficients, through cross-validation to improve generalization ability. Finally, it obtains the stability assessment results after classification, for example, outputting a report showing that "stability is mainly affected by humidity, assessed as moderate risk."

[0088] Understandably, in another embodiment, this method is applied to the field of pipeline protection materials. When calculating the quantification index, electrochemical impedance spectroscopy can be used to measure the loss of the protective coating, with a threshold set at a 15% decrease in impedance value. If the loss falls below the threshold, a support vector machine algorithm classifies potential factors such as exposure to acidic media or temperature fluctuations, resulting in a classification of "high risk of chemical factors," thereby guiding maintenance strategies.

[0089] Specifically, the key to the Support Vector Machine (SVM) algorithm in classifying latent factors lies in the data preparation and model training processes. Data preparation involves extracting historical data from material databases to form a feature matrix, where each row represents a sample and each column corresponds to a latent factor such as pH value or UV intensity. The training process iteratively solves a convex optimization problem to find the optimal hyperplane. For example, if latent factors include mechanical vibration and thermal cycling, the algorithm calculates a decision function, classifying vibration factors as the primary latent factors affecting stability limits. This detailed process ensures the robustness of the classification. Furthermore, the stability assessment results after classification can be presented using visualization tools, such as generating pie charts to show the contribution ratio of each latent factor. In the bridge material scenario, the results might indicate that environmental factors account for 60%, thus providing a basis for design improvements. The output of such assessment results contributes to the long-term durability management of materials.

[0090] In one embodiment, to enhance flexibility, a support vector machine variant with a multi-kernel function can be introduced.

[0091] For example, using a polynomial kernel function to handle complex nonlinear factors results in classification results that more accurately reflect the potential impact of stability limits. When applied to pipe material testing, this variant can better distinguish between short-term runoff and long-term degradation factors.

[0092] It should be noted that, through the above steps, this method demonstrates versatility in the field of material protection, and can adapt to stability assessments in different scenarios without introducing additional complexity.

[0093] S106, S106. Based on the stability evaluation results after classification, the random forest algorithm is used to predict the degree of impact of deep fusion lack on application reliability, and to determine whether the predicted value meets the requirements of outdoor application scenarios, thereby obtaining the final performance verification model.

[0094] Based on the stability assessment results after classification, data features lacking deep fusion (i.e., features with insufficient fusion) are extracted. These features are then processed using a random forest algorithm, where the random forest algorithm takes the data features as input and outputs a predicted value indicating the degree of reliability impact. Based on the predicted value, integrated environmental variable data for outdoor application scenarios is obtained. If the integrated data exceeds a preset threshold, the influencing factor analysis is adjusted using the predicted value (as a reliability basis). This influencing factor analysis extracts key variables from the integrated environmental variable data to determine the threshold range for scenario adaptation verification. For the threshold range, real-time data calibration is used to fuse the integrated environmental variable data with the predicted value. This real-time data calibration uses collected instantaneous environmental indicators to correct the fusion and determine whether it meets the requirements of the outdoor application scenario. A subset meeting the requirements is obtained from the determination, and model fusion optimization is applied to this subset. This model fusion optimization constructs and integrates the feature weights of the subset using a preset set of sub-models to obtain a preliminary performance verification model. Based on the preliminary performance verification model, the model fusion optimization is iteratively optimized using training data obtained from the preliminary performance verification model. This iteration uses the training dataset obtained from the preliminary performance verification model to cyclically adjust the feature weights, resulting in the final performance verification model.

[0095] In one implementation, the classified stability assessment results are first obtained. These results are derived from the classification of the stability of outdoor application equipment such as wind turbine generators.

[0096] Specifically, stability assessment involves collecting operational data of the equipment under different environmental conditions, such as wind speed, temperature, and vibration parameters. This data is then categorized into three classes—stable, critically stable, and unstable—using classification algorithms such as support vector machines. This classification process ensures that the input data for subsequent predictions has a reliable foundation and, further, provides a structured feature set for the random forest algorithm.

[0097] For example, a lack of deep fusion refers to the absence of deep-level feature fusion during multimodal data integration, which affects application reliability. For instance, in outdoor applications such as wind power generation equipment, deep fusion typically involves the deep integration of sensor data such as acoustic and vibration signals. The lack of such fusion can lead to prediction bias.

[0098] It's important to note that the Random Forest algorithm was used to predict the extent of this lack of reliability. This algorithm achieves its predictions by constructing multiple decision trees and voting on the results. The specific process involves: first, extracting feature vectors from the classified stability assessment results. These vectors contain stability classification labels, environmental parameters, and device status indicators. Then, feeding these features into the Random Forest model, during which subsamples and feature subsets are randomly selected during model training to construct the decision tree forest. In this way, the algorithm can quantify the impact of deep fusion lack, for example, calculating the probability of a decrease in reliability.

[0099] In one possible implementation, the prediction process of the random forest algorithm is further refined.

[0100] Preferably, a training dataset is first prepared, based on historical outdoor application data, such as stability records of wind power equipment under strong wind conditions. Each decision tree is randomly sampled from the data, and partial features are selected for splitting, for example, considering the impact of wind speed on vibration when splitting nodes. The ensemble output of multiple trees in the forest is an average prediction value, representing the degree of reliability impact caused by the lack of deep fusion.

[0101] For example, if the predicted value shows a reliability decrease of more than 20%, it indicates that the fusion lacks a significant impact on equipment performance. The advantage of this prediction mechanism lies in its robustness, enabling it to handle outdoor scenarios where noisy data is common. Furthermore, it determines whether the predicted value meets the requirements of outdoor application scenarios. This determination is based on a preset threshold; for example, in the outdoor environment of wind power generation, reliability requirements necessitate that the predicted impact be below a specific threshold, such as 10%.

[0102] Specifically, by comparing the predicted value with a threshold, if the predicted value is less than the threshold, the requirement is considered met; otherwise, the fusion strategy needs to be optimized. This judgment process ensures the practicality of the model, enabling timely identification of potential risks in real-world applications such as monitoring wind turbines.

[0103] In one embodiment, the final performance verification model is obtained by integrating the above steps.

[0104] Specifically, the random forest prediction results are combined with the judgment output to form an end-to-end validation framework. The model takes classification stability results as input and outputs validation conclusions, such as whether it is suitable for outdoor deployment. Through multiple iterations of validation, the model demonstrates high accuracy in simulated outdoor scenarios and can effectively guide equipment maintenance.

[0105] It is understandable that, in another implementation, different outdoor sub-scenes are considered.

[0106] For example, in coastal wind power areas, stability assessments may emphasize salt spray corrosion. In this scenario, the random forest algorithm adjusts feature weights, such as increasing the sampling probability of corrosion-related features, to predict the impact of insufficient deep fusion. During the judgment process, the required threshold is set based on the regional wind intensity to ensure model adaptability.

[0107] Specifically, the lack of predictive details due to insufficient deep fusion needs further explanation. This lack may stem from incomplete data sources, such as sensor signals not being fused using deep neural networks. Random Forest reduces overfitting through a "parallel training, result aggregation" (bagging) approach. The process involves: first, generating multiple training sets through random sampling with replacement, and training a decision tree independently on each set; then, combining the predictions from all trees to make a final judgment. For example, in wind power equipment, if insufficient fusion leads to increased signal noise, the predicted value will reflect a decrease in reliability from 90% to 75%. This detailed process supports model innovation, resulting in more accurate reliability assessments in outdoor applications.

[0108] Preferably, the construction of the performance verification model also includes a verification phase.

[0109] In one embodiment, cross-validation is used to evaluate model performance, such as splitting the dataset into training and test sets and calculating prediction accuracy. The model ultimately outputs a validation report listing the proportion of scenarios that meet the requirements, thus providing decision support for outdoor applications. Furthermore, regarding the feature selection process for random forests, the impact of key features such as wind load on stability is emphasized in outdoor applications.

[0110] For example, the wind load factor is a factor used to calculate the wind pressure that a conductor or equipment can withstand, typically based on wind speed and equipment geometry. The process of calculating the wind pressure value that a conductor can withstand involves first measuring the wind speed v, and then applying the formula P = 0.5 * ρ * v² * C, where P is the wind pressure, ρ is the air density (typically 1.2 kg / m³), and C is the wind load factor, which takes into account the drag coefficient of equipment shapes such as cylindrical conductors, which is approximately 1.2. This wind pressure value, obtained through this calculation, is input into the stability assessment to support subsequent predictions.

[0111] In one possible implementation, the generality of the final model is demonstrated through multiple embodiments.

[0112] For example, in mountainous, windy outdoor scenarios, algorithm parameters are adjusted to address terrain variations, predict the impact of fusion deficiencies, and determine whether durability requirements are met. This diversification ensures the flexible application of the technical solution within the same domain.

[0113] S107. Using the final performance verification model constructed in S106, iteratively adjust the process parameters of the integral molding process, obtain weather resistance improvement data for high-performance optics, and determine the stability optimization scheme of the material in long-term use.

[0114] By adjusting the combination of process parameters under pre-established experimental conditions for the one-piece molding process, the initial optical performance under different parameters was recorded to obtain a preliminary performance data set. Based on this preliminary performance data set, a preliminary performance verification model was constructed. This model uses a linear regression method with the dataset as input, and outputs predicted values ​​by fitting the relationship between environmental variables. This differs from the final performance verification model of S106 (based on random forest and fusion optimization); the former is used for preliminary simulation, while the latter is used for final optimization verification. Different environmental variables were input to simulate long-term use test scenarios, obtaining the material's weather resistance performance values ​​under various conditions. Based on these weather resistance performance values, an environmental adaptability assessment was conducted. This assessment extracted adaptability indicators from the weather resistance data and used high-precision data analysis tools to extract key weather resistance data features, determining the changing trend of the material's weather resistance characteristics. Based on the changing trend of the weather resistance characteristics, a parameter iteration method was used. This method adjusts the process parameter combination based on trend feedback loops, optimizes the one-piece molding process, and obtains improved weather resistance data. Based on the improved weather resistance data, a stability optimization scheme was formulated to ensure process consistency, judge the material's stable performance in long-term use, and complete the verification of the improved weather resistance data.

[0115] In one implementation, process optimization of optical materials is achieved by constructing a performance verification model. First, it is necessary to understand the concept of a performance verification model, which is a framework based on simulation and experimental data used to evaluate the material properties after integral molding.

[0116] Specifically, the model integrates indicators such as optical transmittance, mechanical strength, and environmental tolerance, and outputs predicted performance values ​​by taking process parameters such as temperature, pressure, and molding time as input.

[0117] For example, in optical lens manufacturing, this model can simulate the degradation of materials under ultraviolet exposure, thus providing a basis for subsequent iterations. Furthermore, the model construction process includes data acquisition, parameter fitting, and validation phases. Data acquisition involves extracting samples from historical production records to ensure the model's accuracy. This framework helps identify potential defects in the process and achieve efficient optimization.

[0118] For example, iteratively adjusting the process parameters of the one-piece molding process is a core step.

[0119] It should be noted that integral molding refers to molding optical materials such as polycarbonate or glass matrix in one step through injection molding or extrusion, avoiding interface problems caused by multi-step assembly. The iterative adjustment process is based on the feedback loop of the performance verification model: first, after setting the initial parameters, the model is run to calculate the performance indicators; then, the parameters are adjusted according to the deviation, such as increasing the molding temperature to enhance the uniformity of molecular chain arrangement.

[0120] Specifically, in the production of high-performance optical filters, if the model shows insufficient weather resistance, the proportion of antioxidants can be iteratively increased, and the effect can be verified through multiple simulations. This method ensures the gradual optimization of parameters and is applicable to the molding of various optical components.

[0121] In one possible implementation, obtaining weather resistance improvement data for high-performance optics relies on a dedicated testing and analysis engine. This analysis engine is a data processing system that includes data preprocessing and statistical analysis modules. Weather resistance improvement data refers to the quantified changes in a material's performance under long-term environmental exposure, such as high temperature, humidity, or radiation, for example, transmittance retention or surface hardness decay curves. The specific process is as follows: First, experimental data is collected, such as exposing the molded optical sample to an accelerated aging chamber for a specified time. Then, the engine generates improvement data by calculating the mean and standard deviation, for example, comparing the differences in weather resistance indicators before and after optimization. Furthermore, in the business of optical display materials, this engine can process massive amounts of test point data, output visualized reports, and support decision-making. The introduction of this engine improves data reliability and ensures the objectivity of the acquisition process.

[0122] Preferably, the stability optimization scheme for the material during long-term use is determined based on the aforementioned data and model output. The stability optimization scheme is a set of strategies, including suggestions for material formulation adjustments and process improvements.

[0123] For example, if weathering data shows that the material is not stable enough in humid environments, the solution may recommend adding a hydrophobic coating or modifying the molding pressure to reduce microscopic defects.

[0124] Specifically, the process of determining this approach involves multi-factor analysis: integrating the prediction results of the performance verification model with measured data, prioritizing the optimization points that have the greatest impact on long-term use, such as ensuring that the performance degradation of the material does not exceed 5% over a ten-year period in optical sensor applications. The flexibility of this approach allows for fine-tuning according to different optical scenarios, enhancing the versatility of the technology.

[0125] Understandably, the iterative mechanism of the performance verification model is one of its innovative aspects. Let's explain its principle: the model employs a feedback loop structure, similar to gradient descent in machine learning, but simplified to optimize basic parameters. The process includes initial modeling, error calculation, and parameter updates. For example, after inputting process parameters, the model calculates the difference between the expected weather resistance value and the actual value. If the difference exceeds a threshold, the parameters are adjusted and the simulation is repeated. In optical prism manufacturing, this iteration can be repeated 10 to 20 times until convergence. This mechanism not only improves process accuracy but also reduces material waste in business operations because simulation avoids multiple physical tests. Furthermore, the adjustment of process parameters for one-piece molding needs to consider specific business scenarios. In optical lens production, parameters such as molding speed directly affect the internal stress distribution of the material. The adjustment process is explained in detail: first, the model evaluates the stress value under the current parameters; then, iteratively reduces the speed to distribute stress evenly, avoiding the risk of cracking during long-term use.

[0126] For example, if the initial speed is 50 mm per minute and the model shows high-stress areas, it is gradually reduced to 30 mm per minute, and the improvement in weather resistance data is verified. This refinement ensures the practicality of the solution.

[0127] In one embodiment, the acquisition of weather resistance enhancement data can be extended to multi-environment simulations. The analysis engine processing includes data normalization and trend fitting, such as combining high-temperature data with humidity data to calculate a comprehensive weather resistance index.

[0128] Specifically, the engine first filters out noisy data, and then uses linear regression to estimate the improvement curve. In optical window materials, this helps predict long-term stability and provides a basis for optimization.

[0129] It should be noted that the final determination of the stability optimization scheme emphasizes data-driven approaches. After the scheme is generated, its effectiveness is confirmed through small-scale verification. For example, in optical fiber applications, the optimized material exhibits better fatigue resistance. This method demonstrates versatility within the same field.

[0130] For example, in the overall process of a high-performance optical system, the above steps form a closed loop: starting with model verification, adjusting parameters to acquire data, and finally outputting the solution. This logic ensures continuous optimization of the process.

[0131] In one embodiment, for special optical materials such as fluoride glass, iterative adjustments can introduce additional parameters, such as vacuum control, to further improve weather resistance. Model simulations show that this adjustment process exhibits a significant improvement in stability.

[0132] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the weather resistance of polycarbonate materials, characterized in that, include: Molecular structure data of polycarbonate substrate and environmental erosion parameters for outdoor applications are obtained to construct a fixed distribution model of internal protective components. Based on the fixed distribution model, the chemical composition ratio of the external protective layer is adjusted to determine the optimized formulation for deep fusion. The polycarbonate substrate is integrally molded using the optimized formulation, interface bonding data is extracted, and the risk level is assessed to obtain reinforced material samples. The weather resistance of the reinforced material samples is tested by simulating outdoor application scenarios to obtain quantitative indicators of protective component loss. Based on the quantitative indicators, potential factors affecting material stability are classified to obtain stability assessment results. A predictive algorithm is used to analyze the impact of lack of deep fusion on application reliability, determine whether it meets the requirements of outdoor application scenarios, and construct a performance verification model. The process parameters of the integral molding process are adjusted using the performance verification model to obtain weather resistance improvement data and determine a stability optimization scheme for long-term use.

2. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The process of acquiring molecular structure data of the polycarbonate substrate and environmental erosion parameters for outdoor applications, and constructing a fixed distribution model for the internal protective components, includes: collecting the molecular chain arrangement characteristics of the polycarbonate substrate and the temperature and humidity variation range of the environmental erosion parameters to generate a corresponding dataset; using molecular dynamics simulation to analyze the hydrogen bonding forces between the internal protective components and the polycarbonate substrate, calculating the distance between hydrogen and oxygen atoms, determining the intensity distribution map of the hydrogen bonding forces, and extracting the coordinates of the attachment points of the protective components; obtaining density gradient values ​​from the attachment point coordinates, and determining whether the density difference between adjacent points exceeds a preset threshold; if it does, adjusting the molecular spacing of the internal protective components to obtain the adjusted molecular spacing configuration; using finite element analysis to simulate stability fluctuations based on the interaction response curve between the adjusted molecular spacing configuration and the environmental erosion parameters to determine the fixed mode of the protective components; and constructing a spatial grid framework for the fixed mode of the protective components, integrating substrate bonding strength data to obtain the fixed distribution model of the internal protective components.

3. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The step of adjusting the chemical composition ratio of the outer protective layer according to the fixed distribution model to determine the optimized formulation for deep fusion includes: obtaining chemical composition data of the outer protective layer from the fixed distribution model; measuring the interfacial energy difference between the outer protective layer and the polycarbonate substrate using a scanning electron microscope and surface tension test to obtain an initial energy difference result; comparing the initial energy difference result with a preset threshold, and recording the range of values ​​exceeding the preset threshold to determine the direction of chemical composition adjustment; gradually changing the chemical ratio of the outer protective layer for the specified chemical composition direction, verifying the change in interfacial energy difference after each adjustment using a layered testing method, and obtaining adjusted energy difference data; based on the adjusted energy difference data, combined with substrate matching characteristics and protective layer stability requirements, screening chemical ratio combinations that meet the preset threshold to obtain a preliminary optimized formulation scheme; and for the preliminary optimized formulation scheme, introducing the protective layer thickness influencing factor, and determining the final optimized formulation by simulating the fusion effect under application conditions through hot-pressing composite and interfacial observation.

4. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The process of integrally molding the polycarbonate substrate using the optimized formula, extracting interfacial bonding data, assessing the risk level, and obtaining a reinforced material sample includes: obtaining the initial formula parameters of the polycarbonate substrate from a pre-established formula database; performing integral molding using a hot-pressing device with temperature control to obtain an interfacial bonding strength value; extracting real-time interfacial bonding data using a surface scanning tool via optical imaging based on the interfacial bonding strength value, assessing the risk level of poor interfacial bonding, and obtaining a risk assessment result; if the risk assessment result exceeds a preset threshold, adjusting the additive content in the formula ratio, verifying the adjusted interfacial bonding data through repeated molding and energy dispersive spectroscopy, and determining an optimized formula scheme; reinforcing the polycarbonate substrate using the optimized formula scheme, obtaining fusion depth data through thickness measurement, and obtaining the reinforced material sample.

5. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The step of testing the weather resistance of the enhanced material sample by simulating outdoor application scenarios to obtain quantitative indicators of the loss of protective components includes: using ultraviolet irradiation equipment to simulate outdoor application scenarios for the enhanced material sample and obtaining long-term weather resistance decay curve data; extracting the initial change value of the loss of protective components through component spectrum analysis based on the decay curve data to obtain a loss trend index; determining the intermediate quantitative level of the loss of protective components by combining environmental factor calibration and adjustment quantitative calculations based on the loss trend index; and determining the quantitative index of the loss of protective components from the intermediate quantitative level by repeatedly irradiating and verifying the fusion depth data.

6. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The step of classifying potential factors affecting material stability based on the quantified indicators to obtain stability assessment results includes: collecting data on the loss of protective components from the enhanced material samples and calculating a quantified indicator of the loss of protective components; if the quantified indicator is lower than a preset threshold, obtaining potential factor data from environmental exposure records to determine a factor dataset; applying a support vector machine algorithm to classify potential factors affecting material stability for the factor dataset to obtain classified factor groups; based on the classified factor groups, fusing aging rate indicators obtained from historical aging records to determine the material stability assessment results; extracting key thresholds from the material stability assessment results, generating a protective component replenishment scheme, and obtaining the final optimized stability distribution.

7. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The step of using a predictive algorithm to analyze the impact of lack of deep fusion on application reliability, determine whether it meets the requirements of outdoor application scenarios, and construct a performance verification model includes: processing the data features of lack of deep fusion using a random forest algorithm based on the stability assessment results to obtain a predicted value of the reliability impact; obtaining integrated environmental variable data for the outdoor application scenario based on the predicted value; if the integrated data exceeds a preset threshold, adjusting the influencing factor analysis based on the predicted value to determine the threshold range for scenario adaptation verification; for the threshold range, merging the integrated environmental variable data and the predicted value through real-time data calibration to determine whether it meets the requirements of the outdoor application scenario; obtaining a subset that meets the requirements from the determination, optimizing the subset through model fusion to obtain a preliminary performance verification model; and iterating the preliminary performance verification model through algorithm training data, adjusting feature weights, to obtain the final performance verification model.

8. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The process of adjusting the integrated molding process parameters through the performance verification model to obtain weather resistance improvement data and determine a stability optimization scheme for long-term use includes: adjusting the combination of process parameters for the integrated molding process, recording the initial optical performance under different parameters, and obtaining a preliminary performance data set; constructing a performance verification model based on the preliminary performance data set, fitting the relationship between environmental variables using a linear regression method, outputting predicted values, simulating long-term use test scenarios, and obtaining weather resistance performance values; extracting key weather resistance data features based on the weather resistance performance values ​​and environmental adaptability assessment, and determining the changing trend of the material's weather resistance characteristics; adjusting the combination of process parameters based on the changing trend and using a parameter iteration method to optimize the integrated molding process and obtain improved optical performance data; and formulating a stability optimization scheme based on the improved optical performance data to determine the material's stable performance in long-term use.

9. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The process of acquiring molecular structure data of the polycarbonate substrate and environmental erosion parameters for outdoor applications, and constructing a fixed distribution model for the internal protective components, includes: collecting initial molecular structure data of the polycarbonate substrate, analyzing molecular chain arrangement characteristics, and combining this with the temperature and humidity variation range of the environmental erosion parameters to form a corresponding dataset; calculating the bonding force between the internal protective components and the polycarbonate substrate using molecular dynamics simulation methods for the corresponding dataset, generating an intensity distribution map, and extracting the coordinates of the attachment points of the protective components; analyzing the density gradient values ​​based on the attachment point coordinates, and determining whether the density difference between adjacent points meets a preset threshold; if not, adjusting the intermolecular spacing to obtain an adjusted configuration; calculating the stress distribution by dividing the mesh elements using finite element analysis methods based on the interaction response between the adjusted configuration and the environmental erosion parameters, and determining the fixed mode under stability fluctuations; and constructing a spatial mesh framework for the fixed mode, integrating the bonding strength data, and generating the fixed distribution model.

10. The method for optimizing the weather resistance of polycarbonate materials as described in claim 1, characterized in that, The step of adjusting the chemical composition ratio of the outer protective layer according to the fixed distribution model to determine the optimized formula for deep fusion includes: extracting the chemical composition data of the outer protective layer from the fixed distribution model; measuring the interface energy difference using a pre-established detection tool to obtain initial results; comparing the initial results with a preset threshold; if the initial results exceed the preset threshold, determining the adjustment direction and gradually changing the chemical ratio; verifying the change in the interface energy difference after adjustment using a layered testing method to obtain updated data; based on the updated data, combined with the substrate surface roughness and chemical affinity, as well as the durability and adhesion requirements of the protective layer, selecting a ratio combination that meets the preset threshold to obtain a preliminary scheme; for the preliminary scheme, introducing the thickness influencing factor, verifying the fusion effect through hot-pressing composite and interface observation, and determining the final optimized formula.

Images